Trying to find useful things to do with emerging technologies in open education

Fragment – Outliers in Emergent Social Positioning Maps

This is a placeholder as much as anything, something I want to try out but don’t have time to do right now… The context is the social media mapping approach I’ve been doodling with a few weeks for now, where I try to position social media users in terms of who their followers follow (for example, A Couple More Social Media Positioning Maps for UK HE Twitter Accounts).

One of the problems with the approach is that you often get some of the same-old, same-old accounts appearing again and again (@stephenfry for example). So I’ve been wondering whether it might be worth generating funnel plots that plot the rate at which followers of a target account follow the other accounts identified in the positioning maps generated around the target account? On the x we’d plot the total number of followers of each account, and on the y, the rate at which they are followed by the followers of the target account (i.e. their in-degree in the map divided by the target account follower sample size used to generate the map). We might then get useful signal from the presence of accounts that appear to be over-represented within the target account followers sample, signal that can be used to identify those accounts that are more highly associated with the target account than we might expect by chance?

Another factor that I maybe need to take into account is the total number of accounts followed by the target account followers?

PS by the by, I notice that my map of folk “in the vicinity of the #gdslaunch hashtag” appears to have been posterised…:-)

(If anyone wants SVG or graphml based representations of any of the Gephi generated images I post either here or on my flickr account, it can probably be arranged;-)